Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2010

The uncapacitated facility location problem (FLP) is a mathematical way to optimally locate facilities within a set of candidates such that each facility has no capacity limit in satisfying the requirements of a given set of clients. Particle swarm optimization (PSO) is a population-based optimization technique which operates on a population of potential solutions applying an information sharing approach to produce better and better approximations to a solution. Though hybrid methods have been reported to produce better results, this study used PSO in a stand-alone mode to determine first its potential in finding solutions for uncapacitated FLP particularly when applied to real world data. First, a successful mapping between the method and the problem was established. Then a minimization fitness function to evaluate the solutions was defined which involves penalty for every violated constraint. Upon implementation of the method for the problem, best parameter values to solve the problem were achieved. Results showed that applying PSO for the problem yielded better facility locations compared to the existing ones. However, although these results showed that PSO is a promising method to solve this particular problem, further studies are still needed to improve the results such as by reducing the values of the parameters to fit the small-scaled search space of the data.